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[Perf] Async GPU P2P access cache precomputation to reduce startup time#39249

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mrigasiyer:feat/async-p2p-cache-precompute
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[Perf] Async GPU P2P access cache precomputation to reduce startup time#39249
mrigasiyer wants to merge 3 commits into
vllm-project:mainfrom
mrigasiyer:feat/async-p2p-cache-precompute

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@mrigasiyer mrigasiyer commented Apr 8, 2026

Purpose

Fix startup latency regression when VLLM_SKIP_P2P_CHECK=0: the GPU P2P access check blocks startup for ~60s on multi-GPU CUDA configs because it spawns subprocesses to verify IPC memory access across all GPU pairs before custom allreduce can initialize.

This PR overlaps that subprocess work with model weight loading by running it in a background thread, making it effectively free on any model that takes longer than ~60s to load (most production models at TP≥2).
Partially addresses #19824.

This is not a duplicate of any open PR — checked against open PRs searching for "19824 in:body", "p2p async startup", and "gpu_p2p_access_check async". The related PR #39040 addresses a different aspect (enabling custom allreduce on PCIe topologies).

Test Plan

Ran linter on all changed files:

pre-commit run --files \
  vllm/distributed/device_communicators/all_reduce_utils.py \                                                                                                                                                 
  vllm/distributed/device_communicators/base_device_communicator.py \
  vllm/distributed/device_communicators/cuda_communicator.py \
  vllm/distributed/device_communicators/custom_all_reduce.py \                                                                                                                                                
  vllm/v1/worker/gpu_worker.py

Test Result

ruff check....................................................................Passed
ruff format...................................................................Passed
typos.........................................................................Passed                                                                                                                          
Run mypy locally for lowest supported Python version..........................Passed
Check SPDX headers............................................................Passed                                                                                                                          
Check root lazy imports.......................................................Passed
Check for forbidden imports...................................................Passed
Prevent new 'torch.cuda' APIs call............................................Passed

Essential Elements of an Effective PR Description Checklist
  • The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
  • The test plan, such as providing test command.
  • The test results, such as pasting the results comparison before and after, or e2e results
  • (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.
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When VLLM_SKIP_P2P_CHECK=0, the P2P access check blocks startup for ~60s
on multi-GPU CUDA configs. This overlaps that work with model weight loading.

- Extract subprocess work into _precompute_p2p_access_cache() (thread-safe)
- Defer CustomAllreduce P2P check to finalize_p2p() (collective, called before first allreduce)
- Add finalize_p2p_check() to base communicator and CudaCommunicator
- Launch background thread in Worker.init_device() on local_rank==0
- Join thread and finalize collectively in determine_available_memory()

Partially addresses vllm-project#19824
@mrigasiyer mrigasiyer requested a review from njhill as a code owner April 8, 2026 02:05
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github-actions Bot commented Apr 8, 2026

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Code Review

This pull request optimizes the GPU P2P access check by pre-computing the cache in a background thread during model loading, which helps reduce startup latency. It introduces a deferred initialization mechanism for custom all-reduce, allowing the P2P check to overlap with other initialization tasks. Feedback was provided to ensure the atomicity of the cache file write operation to prevent potential file corruption if the process is interrupted.

Comment thread vllm/distributed/device_communicators/all_reduce_utils.py Outdated
  Write to a temp file then os.replace() to avoid leaving a corrupted
  JSON if the process is interrupted mid-write.
@mrigasiyer mrigasiyer force-pushed the feat/async-p2p-cache-precompute branch from 0df3088 to 7d10262 Compare April 13, 2026 19:48
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mergify Bot commented May 23, 2026

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @mrigasiyer.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

@mergify mergify Bot added the needs-rebase label May 23, 2026
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